Wiring networks diagnosis using K-Nearest neighbour classifier and dynamic time warping

The similarity between a pair of time series, i.e., sequences of indexed values in time order, is often estimated by the dynamic time warping (DTW) distance, instead of any in the well-studied family of measures including the longest common subsequence (LCS) length and the edit distance. Although it may seem as if the DTW and the LCS(-like) measures are essentially different, we reveal that the DTW distance can be represented by the longest increasing subsequence (LIS) length of a sequence of integers, which is the LCS length between the integer sequence and itself sorted. For a given pair of time series of n integers between zero and c, we propose an integer sequence that represents any substring-substring DTW distance as its band-substring LIS length. The length of the produced integer sequence is O(c⁴ n²) or O(c² n²) depending on the variant of the DTW distance used, both of which can be translated to O(n²) for constant cost functions. To demonstrate that techniques developed under the LCS(-like) measures are directly applicable to analysis of time series via our reduction of DTW to LIS, we present time-efficient algorithms for DTW-related problems utilizing the semi-local sequence comparison technique developed for LCS-related problems.

Dynamic time warping(DTW) distance is the most effective similarity measurement methods in time-series data mining area. Because of the high computational complexity of DTW, it is not suitable for huge amounts of data. Thus, some DTW distance lower bound methods are proposed, which can improve the efficiency of DTW distance calculation. In this paper, we summarized the existing lower bound of DTW method and then proposed a closely related field with the application and exacted Lower Bounding distance measure with Segmentation(eLBS). Experiment results show that this method meet the need of non-omission.

Background: Data on early childhood disease collected in clinics has accumulated into big data. Those data can be used for classification of early childhood diseases to help medical staff in diagnosing diseases that attack early childhoods.Objective: This study aims to apply Principal Component Analysis (PCA) and K-Nearest Neighbor (K-NN) Classifier for the classification of early childhood diseases.Methods: Data analysis was performed using PCA to obtain variables that had a major influence on the classification of early childhood diseases. PCA was done by observing the correlation between variables and eliminating variables that have little influence on classification. Furthermore, data on early childhood disease was classified using the K-Nearest Neighbor Classifier method.Results: The results of system evaluation using 150 test data indicated that the classification system by applying PCA and KNN Classifier had an accuracy value of 86%.Conclusion: PCA can be used to reduce the number of variables involved so that it can improve system performance in terms of efficiency. In addition, the application of PCA and KNN can also improve accuracy in the classification of early childhood diseases.

Spectrum sensing use current primary user (PU) received signal to detect PU channel state at current time, however PU channel state in near future using previous detected channel state information can secure an efficient utilization of unoccupied spectrum, also can help in preventing harmful interference with PU and solving problem of latency between spectrum sensing and data transmission which caused by hardware implementation. In this paper, an algorithm Based on K-Nearest Neighbor (KNN) Classifier and Kalman Filter is proposed to detect and predict future of primary user channel state the spectrum holes. The new algorithm based on training KNN classifier over a set containing some frame time slots energy test statistics along with their corresponding decisions about presence or absence of primary user transmission, so as to predict decisions for new unclassified time slot, then utilize Kalman filter to track PU primary user channel state by next expected spectrum holes based on previously detected hole. Experimental results show that proposed approach is efficient and easy to implement for detecting and tracking predicting PU channel state. KEYWORD: Channel state tracking; Channel state prediction; K-nearest neighbor classifier; Kalman Filter; Spectrum sensing time slot

The similarity between a pair of time series, i.e., sequences of indexed values in time order, is often estimated by the dynamic time warping (DTW) distance, instead of any in the well-studied family of measures including the longest common subsequence (LCS) length and the edit distance. Although it may seem as if the DTW and the LCS(-like) measures are essentially different, we reveal that the DTW distance can be represented by the longest increasing subsequence (LIS) length of a sequence of integers, which is the LCS length between the integer sequence and itself sorted. For a given pair of time series of length n such that the dissimilarity between any elements is an integer between zero and c, we propose an integer sequence that represents any substring-substring DTW distance as its band-substring LIS length. The length of the produced integer sequence is O(c n^2), which can be translated to O(n^2) for constant dissimilarity functions. To demonstrate that techniques developed under the LCS(-like) measures are directly applicable to analysis of time series via our reduction of DTW to LIS, we present time-efficient algorithms for DTW-related problems utilizing the semi-local sequence comparison technique developed for LCS-related problems.

Solid waste bin level detection using gray level co-occurrence matrix feature extraction approach

Recently, computer and smartphone devices have been increasingly employed in various life aspects. Human Activity Recognition (HAR) is one of the most emerging fields that can be clearly noticed in terms of computer related researches. This study proposes a classification model to recognize human six basic activities based on smartphone embedded sensors. For feature selection, we utilized three optimization search algorithms which are: Particle Swarm Optimization (PSO), Greedy, and Genetic algorithm. K-nearest neighbors (KNN) classifier, with Manhattan distance function, is adopted to measure the models performance on two publicly available well-known datasets (i.e., WISDM and UCI-HAR). All experiments are achieved using 10-folds cross validation technique, and the results are evaluated and compared using different metrics, for instance, accuracy, root mean square error, f-measure, and number of features. The experimental results proved that PSO based KNN classifier presents the best performance compared to the other two algorithms, in addition to the baseline KNN classifier. Moreover, the proposed model outperforms those state-of-the-art models presented in related literature regarding the UCI-HAR dataset with performance rate of 98.89%, and close to the best performance achieved on WISDM dataset with performance rate of 91.07%.

This paper presents automatic detection and localization of myocardial infarction (MI) using K-nearest neighbor (KNN) classifier. Time domain features of each beat in the ECG signal such as T wave amplitude, Q wave and ST level deviation, which are indicative of MI, are extracted from 12 leads ECG. Detection of MI aims to classify normal subjects without myocardial infarction and subjects suffering from Myocardial Infarction. For further investigation, Localization of MI is done to specify the region of infarction of the heart. Total 20,160 ECG beats from PTB database available on Physio-bank is used to investigate the performance of extracted features with KNN classifier. In the case of MI detection, sensitivity and specificity of KNN is found to be 99.9% using half of the randomly selected beats as training set and rest of the beats for testing. Moreover, Arif-Fayyaz pruning algorithm is used to prune the data which will reduce the storage requirement and computational cost of search. After pruning, sensitivity and specificity are dropped to 97% and 99.6% respectively but training is reduced by 93%. Myocardial Infarction beats are divided into ten classes based on the location of the infarction along with one class of normal subjects. Sensitivity and Specificity of above 90% is achieved for all eleven classes with overall classification accuracy of 98.8%. Some of the ECG beats are misclassified but interestingly these are misclassified to those classes whose location of infarction is near to the true classes of the ECG beats. Pruning is done on the training set for eleven classes and training set is reduced by 70% and overall classification accuracy of 98.3% is achieved. The proposed method due to its simplicity and high accuracy over the PTB database can be very helpful in correct diagnosis of MI in a practical scenario.

When a single-phase grounding fault occurs in a resonant grounding system, the determination of the fault location remains a significant challenge due to the small fault current and the instability of the grounding arc. In order to solve the problem of low protection sensitivity when a high-resistance grounding fault occurs in a resonant grounding system, this paper proposes a fault location method based on the combination of dynamic time warping (DTW) distance and fuzzy C-means (FCM) clustering. By analyzing the characteristics of the zero-sequence current upstream and downstream of the fault point when a single-phase grounding fault occurs in the resonant grounding system, it is concluded that the waveform similarity on both sides of the fault point is low. DTW distance can be used to measure the similarity of two time series, and has the characteristics of good fault tolerance and synchronization error tolerance. According to the rule that the DTW value of faulty section is much larger than that of nonfaulty sections, FCM clustering is used to classify the DTW value of each section. The membership degree matrix and cluster centers are obtained. In the membership degree matrix, the section corresponding to the data in a class of their own is the faulty section, and all other data correspond to the nonfaulty section; otherwise, it is a fault occurring at the end of the line. The simulation results of MATLAB/Simulink and the field data test show that the method can accurately locate the faulty section.

Synchronized spontaneous low frequency fluctuations of the so called BOLD signal, as measured by functional Magnetic Resonance Imaging (fMRI), are known to represent the functional connections of different brain areas. Dynamic Time Warping (DTW) distance can be used as a similarity measure between BOLD signals of brain regions as an alternative of the traditionally used correlation coefficient and the usage of the DTW algorithm has further advantages: beside the DTW distance, the algorithm generates the warping path, i.e. the time-delay function between the compared two time-series. In this paper, we propose to use the relative length of the warping path as classification feature and demonstrate that the warping path itself carries important information when classifying patients according to cannabis addiction. We discuss biomedical relevance of our findings as well.

This paper presents the combination of electroencephalogram (EEG) power spectrum ratio and Spectral Centroids techniques to extract unique features for human stress from EEG signals. The combination of these techniques was able to improve the k-NN (k-Nearest Neighbor) classifier accuracy to detect and classify human stress from two cognitive states, Close-eye (CE) and Open-eye (OE). The EEG power spectrum in term of Energy Spectral Density (ESD) for each frequency bands (Delta, Theta, Alpha and Beta) was calculated. The ratio of EEG power spectrum and the average value of Spectral Centroids were selected as features to k-Nearest Neighbor (k-NN). The training and testing of the classifier were evaluated at 50:50 ratios and 70:30 ratios. The results showed that the combination of EEG power spectrum and Spectral Centroids techniques with the training and testing of k-NN set at 70:30 able to detect and classify the unique features for human stress at 88.89% accuracy.

While advances in sensor and signal processing techniques have provided effective tools for quantitative research on traditional Chinese pulse diagnosis (TCPD), the automatic classification of pulse waveforms is remained a difficult problem. To address this issue, this paper proposed a novel edit distance with real penalty (ERP)-based k-nearest neighbors (KNN) classifier by referring to recent progresses in time series matching and KNN classifier. Taking advantage of the metric property of ERP, we first develop a Gaussian ERP kernel, and then embed it into kernel difference-weighted KNN classifier. The proposed Gaussian ERP kernel classifier is evaluated on a dataset which includes 2470 pulse waveforms. Experimental results show that the proposed classifier is much more accurate than several other pulse waveform classification approaches.

The K-Nearest Neighbour (KNN) classification is a standard method in pattern recognition and is especially marked out by its simplicity [128, 148, 305, 370, 374, 379, 389].

In this paper, we propose a novel framework of localization algorithm based on Compressive Sensing (CS) by K-Nearest Neighbor (KNN) classification in Wireless Sensor Networks (WSNs), which can effectively alleviate the problem of off-grid. Many papers about CS-based localization assume that each target is in the center of the grid. However, the assumption is almost impossible. In this paper, the targets can be located anywhere in the area. The target location is regarded as an unknown sparse signal in the discrete spatial domain. Practically, the recovered discrete signal is not exactly K-sparse. In order to make high level of localization accuracy, we localize the targets in three different situations and choose the cluster region according to the distribution of the power of the targets. Then for each classification, we obtain the final location by KNN. This approach can make great use of the location information and compensate the error induced by the problem of off-grid. Finally, simulation and performance analysis show the effectiveness and robustness of our proposed localization algorithm.

Abstract- This study uses a K-Nearest Neighbours (KNN) classifier to detect teenage cardiac disease. Years of age, Sexual orientation, Chest Ache category, Ambient Heart pressure, cholesterol levels, Highest Cardiac Level, and Exercise-Induced Angina are clinically important and interpretable aspects of the cardiovascular disease dataset used to create the model. These qualities were chosen to improve model interpretability for doctors and laypeople. Data preparation encoded categorical variables and standardised features for model optimisation. Histograms, charts with bars, and scatter plots were used to explore feature distribution and heart disease status. The kernel nearest neighbour (KNN) model was trained with k=11 neighbours and tested using precision, a matrix of confusion, classifying report, ROC spectrum, and precision-recall curve, proving predictive power. To select the most influential predictors, feature importance was permutation-based. A Flask web application lets users input health parameters to forecast heart disease risk. The software generates personalised, patient-friendly health information using OpenAI's API and generates PDF reports. The technology keeps prognosis records in an information system for future reference. This approach uses data-driven modelling, interpretability, and user-friendliness to help adolescents recognise and understand heart disease risk. Keywords - Adolescent heart disease identification, risk factors for heart disease, KNN classifier, artificial intelligence, data preprocessing, feature relevance, model assessment. Flask web app, OpenAI integration into the API, health insights, PDF reports, early diagnosis, and healthcare information visualisation.